What is the benefit of Product Quantization (PQ)?

PQ significantly reduces the memory footprint of your vector database (often by 16x to 64x) by compressing vectors into compact codes, which is essential for scaling to millions or billions of vectors.

How does efSearch impact my application performance?

Increasing efSearch improves search recall (accuracy) by exploring more of the graph, but it also increases search latency. It can usually be tuned at query time without rebuilding the index.

When should I use HNSW over Flat indexing?

Use HNSW for datasets larger than 10,000 vectors where search speed is prioritized over 100% exact match accuracy, as it provides approximate nearest neighbor search at much higher speeds.

Can this skill help with specific vector databases like Qdrant?

Yes, this skill includes specialized configuration templates for popular vector engines like Qdrant as well as general libraries like HNSWlib.

Vector Index Optimization

Name: Vector Index Optimization
Author: EngineerWithAI

byEngineerWithAI

데이터 과학 및 ML

Optimizes vector database indexes for production performance by balancing search latency, recall accuracy, and memory consumption.

소개

This skill provides specialized guidance for fine-tuning vector search infrastructure in RAG and AI applications. It helps developers navigate the complex trade-offs of vector indexing by providing templates for HNSW parameter tuning, implementing quantization strategies like Scalar and Product Quantization (PQ), and estimating memory requirements. Whether you are scaling to billions of vectors or reducing latency for a real-time application, this skill offers the benchmarks and implementation patterns needed to maintain high-performance search capabilities.

주요 기능

Vector memory usage and storage estimation tools
Implementation of Scalar (INT8) and Binary quantization
Benchmarking templates for search recall and latency
HNSW parameter optimization for M and efConstruction
Dataset-scale based index type selection (IVF, PQ, HNSW)
0 GitHub stars

사용 사례

Optimizing RAG application search performance for production
Scaling vector search infrastructure to handle millions of embeddings
Reducing memory overhead in cloud-hosted vector databases

소개

주요 기능

Vector memory usage and storage estimation tools
Implementation of Scalar (INT8) and Binary quantization
Benchmarking templates for search recall and latency
HNSW parameter optimization for M and efConstruction
Dataset-scale based index type selection (IVF, PQ, HNSW)
0 GitHub stars

사용 사례

Optimizing RAG application search performance for production
Scaling vector search infrastructure to handle millions of embeddings
Reducing memory overhead in cloud-hosted vector databases